Automatic License Plate Reading (“ALPR”) systems are used by security and law enforcement personnel to find and read vehicle license plate numbers in images produced by video cameras and still cameras. Unfortunately, a number of challenging noise sources are present in most real-world license plate images, such as, for example: heavy shadows, non-uniform illumination (e.g., from one vehicle to the next, daytime versus nighttime, etc.), challenging optical geometries (e.g., tilt, shear, or projective distortions), plate frames and/or stickers partially touching characters, partial occlusion of characters (e.g. trailer hitch ball), poor contrast, and general image noise (e.g. salt and pepper noise). Under these realistic noise conditions, it can be extremely challenging to accurately extract only the sub-image containing the license plate characters.
A particularly challenging aspect of this problem involves horizontal cropping of the license plate sub-image. Here, border artifacts, logos and special symbols, and strong image content outside the actual license plate region can prove particularly difficult. These extraneous image artifacts are often not easy to differentiate from valid characters, especially a “1,” and can substantially impact subsequent character segmentation and recognition.
Prior proposed solutions are typically not fine-grained enough to be used for tight horizontal cropping and instead rely on inaccurate, coarse plate localization (i.e. “the plate is in here somewhere” type of result). Classifiers used in prior approaches for localization are typically constructed to recognize entire license plates, rather than individual symbols and characters.
Therefore, a need exists for a method and system to robustly crop and accurately recognize license plates to account for noise sources and interfering artifacts.